Dynamic Regime-Aware Conformal Prediction (DRACP) is a new method that blends density‑ratio estimation, localized kernel weighting, and probabilistic regime‑aware weighting with a self‑tuning online significance controller to produce reliable prediction intervals under multiple distribution shifts. The authors prove finite‑sample validity with oracle weights, provide a coverage‑gap bound for estimated weights, and give deterministic or regret guarantees for the online controller. In experiments on 48 real forecasting series—including euro‑area inflation, US macroeconomic and energy indicators, and daily financial data—DRACP achieves the most reliable calibration, maintaining coverage close to the nominal 0.90 and never falling below 0.80, while other methods achieve narrower intervals but with higher under‑coverage.
whyItMatters":"DRACP offers a principled trade‑off between calibration and efficiency, ensuring that prediction intervals meet coverage standards even when economic data exhibit covariate shift, concept drift, and latent regimes."
By Bogdan Oancea
Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions. Recent time series CP methods improve local calibration using recent, weighted, or localized residuals.
arXiv:2608. 10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions.
By Sangjin Jin, Kangmin Kim, Junhyeong Lee, Yongjae Lee
SPACE is a conformal wrapper that creates ellipsoidal joint prediction regions for multivariate time‑series forecasts by estimating time‑local covariance directly from the current forecast sample cloud. It calibrates the region’s radius using a dynamic backward window‑selection scheme, avoiding reliance on historical residuals. Experiments on diverse datasets show that SPACE improves joint and rolling coverage, achieving better coverage‑efficiency tradeoffs than existing wrappers.
By Baishi Li, Kelvin J. L. Koa, Ke-Wei Huang
arXiv:2606. 09473v1 Announce Type: cross Abstract: Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted.
By Valery Manokhin
arXiv:2608. 08675v1 Announce Type: new Abstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality.
By Xuan-May Le, Minh-Tuan Tran, Ling Luo, Uwe Aickelin, Dinh Phung, Trung Le
arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
By Ruipu Li, Daniel Menacho, Alexander Rodr\'iguez
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
By Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar, Pascal Van Hentenryck
arXiv:2606. 16356v1 Announce Type: new Abstract: We study uncertainty quantification for aggregated forecasting tasks such as annual totals and year-over-year growth rates.
By Andro Sabashvili
Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instances remain inherently difficult to predict. Reject option mechanisms, which allow models to abstain from high-risk predictions, are well established in classification and regression but underexplored in forecasting.
arXiv:2606. 31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.
By Keivan Faghih Niresi, Alice Cicirello, Olga Fink
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.
By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman